Introduction
本文为斯坦福大学CS231n课程作业及总结，若有错误，欢迎指正。
所有代码均已上传到GitHub项目cs231n-assignment1
Code
1. 训练SVM分类器（多个学习率和正则化强度的准确率）
实现思路:
- 多次循环计算即可
################################################################################
# TODO: #
# Use the validation set to set the learning rate and regularization strength. #
# This should be identical to the validation that you did for the SVM; save #
# the best trained classifer in best_svm. You might also want to play #
# with different numbers of bins in the color histogram. If you are careful #
# you should be able to get accuracy of near 0.44 on the validation set. #
################################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
svm = LinearSVM()
for learning_rate in learning_rates:
for reg in regularization_strengths:
loss_hist = svm.train(X_train_feats, y_train, learning_rate=learning_rate, reg=reg,
num_iters=3000, verbose=False)
y_train_pred = svm.predict(X_train_feats)
train_accuracy = np.mean(y_train == y_train_pred)
y_val_pred =svm.predict(X_val_feats)
val_accuracy = np.mean(y_val == y_val_pred)
results[(learning_rate,reg)] = (train_accuracy,val_accuracy)
if val_accuracy>best_val:
best_val = val_accuracy
best_svm = svm
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
2.训练双层神经网络（参数自定）
- 实现思路：多个超参数多次循环进行训练，并计算val精度
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
learning_rates = [1e-2,1e-1]
regs = [5e-6]
num_iter = [1000,2000]
best_val = 0
for num_iters in num_iter:
for learning_rate in learning_rates:
for reg in regs:
net.train(X_train_feats,y_train,X_val_feats,y_val,learning_rate=learning_rate,
learning_rate_decay=0.95,reg=reg,num_iters=num_iters,batch_size=200,verbose=True)
y_val_pred = net.predict(X_val_feats)
val_acc = np.mean(y_val == y_val_pred)
if val_acc > best_val:
best_val = val_acc
best_net = net
print('Temp best validation accuracy:',val_acc,'\t','best hyper param: ',[num_iter,learning_rate,reg])
# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****Summary
本次作业主要是体会特征改变带来的变化，本身难度并不高，与之前作业部分一致。
